{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<small><i>This notebook was put together by [Jake Vanderplas](http://www.vanderplas.com). Source and license info is on [GitHub](https://github.com/jakevdp/sklearn_tutorial/).</i></small>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Scikit-Learn: Machine Learning with Python\n",
    "\n",
    "This session will cover the basics of Scikit-Learn, a popular package containing a collection of tools for machine learning written in Python. See more at http://scikit-learn.org."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outline\n",
    "\n",
    "**Main Goal:** To introduce the central concepts of machine learning, and how they can be applied in Python using the Scikit-learn Package.\n",
    "\n",
    "- Definition of machine learning\n",
    "- Data representation in scikit-learn\n",
    "- Introduction to the Scikit-learn API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About Scikit-Learn\n",
    "\n",
    "[Scikit-Learn](http://github.com/scikit-learn/scikit-learn) is a Python package designed to give access to **well-known** machine learning algorithms within Python code, through a **clean, well-thought-out API**. It has been built by hundreds of contributors from around the world, and is used across industry and academia.\n",
    "\n",
    "Scikit-Learn is built upon Python's [NumPy (Numerical Python)](http://numpy.org) and [SciPy (Scientific Python)](http://scipy.org) libraries, which enable efficient in-core numerical and scientific computation within Python. As such, scikit-learn is not specifically designed for extremely large datasets, though there is [some work](https://github.com/ogrisel/parallel_ml_tutorial) in this area.\n",
    "\n",
    "For this short introduction, I'm going to stick to questions of in-core processing of small to medium datasets with Scikit-learn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is Machine Learning?\n",
    "\n",
    "In this section we will begin to explore the basic principles of machine learning.\n",
    "Machine Learning is about building programs with **tunable parameters** (typically an\n",
    "array of floating point values) that are adjusted automatically so as to improve\n",
    "their behavior by **adapting to previously seen data.**\n",
    "\n",
    "Machine Learning can be considered a subfield of **Artificial Intelligence** since those\n",
    "algorithms can be seen as building blocks to make computers learn to behave more\n",
    "intelligently by somehow **generalizing** rather that just storing and retrieving data items\n",
    "like a database system would do.\n",
    "\n",
    "We'll take a look at two very simple machine learning tasks here.\n",
    "The first is a **classification** task: the figure shows a\n",
    "collection of two-dimensional data, colored according to two different class\n",
    "labels. A classification algorithm may be used to draw a dividing boundary\n",
    "between the two clusters of points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Ew1Uy1BZJKAgTQkg3/cqI33rrLajVasycORNPP/100sfpdMr+vN2Ak4/3SVYshXWXGa4e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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import the example plot from the figures directory\n",
    "from fig_code import plot_sgd_separator\n",
    "plot_sgd_separator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This may seem like a trivial task, but it is a simple version of a very important concept.\n",
    "By drawing this separating line, we have learned a model which can **generalize** to new\n",
    "data: if you were to drop another point onto the plane which is unlabeled, this algorithm\n",
    "could now **predict** whether it's a blue or a red point.\n",
    "\n",
    "If you'd like to see the source code used to generate this, you can either open the\n",
    "code in the `figures` directory, or you can load the code using the `%load` magic command:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next simple task we'll look at is a **regression** task: a simple best-fit line\n",
    "to a set of data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PPsI//vEPREZG9vp+g6HN6TVoNKGoq2tx+naliGPhiOPhiONxldhj0dZhxabcIvx4uhoK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\n2OoWMlV+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fig_code import plot_linear_regression\n",
    "plot_linear_regression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, this is an example of fitting a model to data, such that the model can make\n",
    "generalizations about new data.  The model has been **learned** from the training\n",
    "data, and can be used to predict the result of test data:\n",
    "here, we might be given an x-value, and the model would\n",
    "allow us to predict the y value.  Again, this might seem like a trivial problem,\n",
    "but it is a basic example of a type of operation that is fundamental to\n",
    "machine learning tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Representation of Data in Scikit-learn\n",
    "\n",
    "Machine learning is about creating models from data: for that reason, we'll start by\n",
    "discussing how data can be represented in order to be understood by the computer.  Along\n",
    "with this, we'll build on our matplotlib examples from the previous section and show some\n",
    "examples of how to visualize data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most machine learning algorithms implemented in scikit-learn expect data to be stored in a\n",
    "**two-dimensional array or matrix**.  The arrays can be\n",
    "either ``numpy`` arrays, or in some cases ``scipy.sparse`` matrices.\n",
    "The size of the array is expected to be `[n_samples, n_features]`\n",
    "\n",
    "- **n_samples:**   The number of samples: each sample is an item to process (e.g. classify).\n",
    "  A sample can be a document, a picture, a sound, a video, an astronomical object,\n",
    "  a row in database or CSV file,\n",
    "  or whatever you can describe with a fixed set of quantitative traits.\n",
    "- **n_features:**  The number of features or distinct traits that can be used to describe each\n",
    "  item in a quantitative manner.  Features are generally real-valued, but may be boolean or\n",
    "  discrete-valued in some cases.\n",
    "\n",
    "The number of features must be fixed in advance. However it can be very high dimensional\n",
    "(e.g. millions of features) with most of them being zeros for a given sample. This is a case\n",
    "where `scipy.sparse` matrices can be useful, in that they are\n",
    "much more memory-efficient than numpy arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Data Layout](images/data-layout.png)\n",
    "\n",
    "(Figure from the [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Simple Example: the Iris Dataset\n",
    "\n",
    "As an example of a simple dataset, we're going to take a look at the\n",
    "iris data stored by scikit-learn.\n",
    "The data consists of measurements of three different species of irises.\n",
    "There are three species of iris in the dataset, which we can picture here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Setosa\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Versicolor\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Virginica\n"
     ]
    }
   ],
   "source": [
    "from IPython.core.display import Image, display\n",
    "display(Image(filename='images/iris_setosa.jpg'))\n",
    "print(\"Iris Setosa\\n\")\n",
    "\n",
    "display(Image(filename='images/iris_versicolor.jpg'))\n",
    "print(\"Iris Versicolor\\n\")\n",
    "\n",
    "display(Image(filename='images/iris_virginica.jpg'))\n",
    "print(\"Iris Virginica\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick Question:\n",
    "\n",
    "**If we want to design an algorithm to recognize iris species, what might the data be?**\n",
    "\n",
    "Remember: we need a 2D array of size `[n_samples x n_features]`.\n",
    "\n",
    "- What would the `n_samples` refer to?\n",
    "\n",
    "- What might the `n_features` refer to?\n",
    "\n",
    "Remember that there must be a **fixed** number of features for each sample, and feature\n",
    "number ``i`` must be a similar kind of quantity for each sample."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the Iris Data with Scikit-Learn\n",
    "\n",
    "Scikit-learn has a very straightforward set of data on these iris species.  The data consist of\n",
    "the following:\n",
    "\n",
    "- Features in the Iris dataset:\n",
    "\n",
    "  1. sepal length in cm\n",
    "  2. sepal width in cm\n",
    "  3. petal length in cm\n",
    "  4. petal width in cm\n",
    "\n",
    "- Target classes to predict:\n",
    "\n",
    "  1. Iris Setosa\n",
    "  2. Iris Versicolour\n",
    "  3. Iris Virginica\n",
    "  \n",
    "``scikit-learn`` embeds a copy of the iris CSV file along with a helper function to load it into numpy arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "[ 5.1  3.5  1.4  0.2]\n"
     ]
    }
   ],
   "source": [
    "n_samples, n_features = iris.data.shape\n",
    "print((n_samples, n_features))\n",
    "print(iris.data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "(150,)\n"
     ]
    }
   ],
   "source": [
    "print(iris.data.shape)\n",
    "print(iris.target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "print(iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "print(iris.target_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data is four dimensional, but we can visualize two of the dimensions\n",
    "at a time using a simple scatter-plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ftyWECF513kxs3749r776KjfccAMKhYKvv/6atm3b+iG04KIM0Xlfp/PtkjaA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\ne6PRaFAoFPTv37/GY9Mnr14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BN2syqxP2qf5h3focLBYrDoeTNWsPVifsk2x2J+s3ZGC1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\nCCEy8f8oeoZGeklrwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x_index = 0\n",
    "y_index = 1\n",
    "\n",
    "# this formatter will label the colorbar with the correct target names\n",
    "formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])\n",
    "\n",
    "plt.scatter(iris.data[:, x_index], iris.data[:, y_index],\n",
    "            c=iris.target, cmap=plt.cm.get_cmap('RdYlBu', 3))\n",
    "plt.colorbar(ticks=[0, 1, 2], format=formatter)\n",
    "plt.clim(-0.5, 2.5)\n",
    "plt.xlabel(iris.feature_names[x_index])\n",
    "plt.ylabel(iris.feature_names[y_index]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick Exercise:\n",
    "\n",
    "**Change** `x_index` **and** `y_index` **in the above script\n",
    "and find a combination of two parameters\n",
    "which maximally separate the three classes.**\n",
    "\n",
    "This exercise is a preview of **dimensionality reduction**, which we'll see later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other Available Data\n",
    "They come in three flavors:\n",
    "\n",
    "- **Packaged Data:** these small datasets are packaged with the scikit-learn installation,\n",
    "  and can be downloaded using the tools in ``sklearn.datasets.load_*``\n",
    "- **Downloadable Data:** these larger datasets are available for download, and scikit-learn\n",
    "  includes tools which streamline this process.  These tools can be found in\n",
    "  ``sklearn.datasets.fetch_*``\n",
    "- **Generated Data:** there are several datasets which are generated from models based on a\n",
    "  random seed.  These are available in the ``sklearn.datasets.make_*``\n",
    "\n",
    "You can explore the available dataset loaders, fetchers, and generators using IPython's\n",
    "tab-completion functionality.  After importing the ``datasets`` submodule from ``sklearn``,\n",
    "type\n",
    "\n",
    "    datasets.load_ + TAB\n",
    "\n",
    "or\n",
    "\n",
    "    datasets.fetch_ + TAB\n",
    "\n",
    "or\n",
    "\n",
    "    datasets.make_ + TAB\n",
    "\n",
    "to see a list of available functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Type datasets.fetch_<TAB> or datasets.load_<TAB> in IPython to see all possibilities\n",
    "\n",
    "# datasets.fetch_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# datasets.load_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next section, we'll use some of these datasets and take a look at the basic principles of machine learning."
   ]
  }
 ],
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